By Victor Mburu, Portfolio Analyst, M-TIBA

A claim that takes 90 days to close changes what an insurer knows. The scheme looks one way on paper and another way in reality, and the gap between the two is where margin disappears.

From claim creation to payment, every day that passes determines whether an insurer is managing their portfolio or only reviewing and approving the costs.

A legacy system’s insurance claim pipeline can take more than 90 days from creation to conclusion. By the time that data reaches a portfolio review, a full quarter has passed, and the trends visible in the data may no longer match what is happening in a particular scheme. Provider behaviour shifts, member utilisation changes, and cost drivers move, which means a decision based on 90-day-old data risks using patterns that are already stale.

A 30-day view is different. The change within 30 days is usually small enough to act on, and the data is still close enough to reality to support a decision. That difference changes what a portfolio analyst can do.

For instance, when fraud is detected late, the claims have already been paid. The insurer works in reverse: identify who was affected, identify the claims involved, calculate the amount at risk, then attempt recovery from the provider. Recovery is expensive and uncertain. The money has left the scheme, and the leverage has shifted.

When the same pattern is caught before payment, the claim can be wrong re-evaluated and corrected, the risk is averted, and further losses are prevented. The operational cost is lower, and the financial outcome is better. The difference between these two scenarios is real time connected data.

Claim adjudication depends on scheme-specific policies. In a manual environment, assessors rely on memory and line-by-line checks. Even a highly experienced assessor cannot easily spot a trend across thousands of claims or compare one provider's billing pattern to the market.

Automated processes flag items that violate scheme policies and flag patterns that are off the market trend. They shorten the time from claim raised to final adjudication.

After adjudication, the same data feeds scheme performance decisions. Without automation, flags arrive late. If a policy runs for one year and the trend is only visible at month six, the scheme has already lost six months.

For example, a scheme worth KES 100 million loses KES 2 million per month to leakage or fraud instances. If the insurer sees the trend at month six, the loss is already KES 12 million, or 12 percent of the scheme. If the same processes were automated, the flags would be detected and addressed in real times weeks, and the KES 12 million could be savings.

This is the arithmetic that plays out across health portfolios that depend on delayed data.

Manual processes become a structural problem when the business grows. More members mean more claims to process, more claims mean more data, and more data means more complexity and more people involved in handling it. Each manual step adds error and each error adds cost, with the losses accumulating quietly inside the claim cycle.  

Automation reduces the number of manual steps, improves accuracy, and prevents losses that would mostly be attributed to human error.
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